Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [14]:
data_dir = '/data'
!pip install matplotlib==2.0.2
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Requirement already satisfied: matplotlib==2.0.2 in /opt/conda/lib/python3.6/site-packages
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib==2.0.2)
Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
You are using pip version 9.0.1, however version 18.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [15]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[15]:
<matplotlib.image.AxesImage at 0x7f0b14344358>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [16]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[16]:
<matplotlib.image.AxesImage at 0x7f0b1796dc18>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [17]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [18]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    r_input = tf.placeholder(dtype=tf.float32, shape=(None, image_width, image_height, image_channels))
    z_input = tf.placeholder(dtype=tf.float32,shape=(None, z_dim))
    learning_rate = tf.placeholder(dtype=tf.float32)

    return r_input, z_input, learning_rate

    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 497, in start\n    self.io_loop.start()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 832, in start\n    self._run_callback(self._callbacks.popleft())', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 605, in _run_callback\n    ret = callback()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 536, in <lambda>\n    self.io_loop.add_callback(lambda : self._handle_events(self.socket, 0))', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events\n    self._handle_recv()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback\n    callback(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2907, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2961, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-18-485bf58e1893>", line 25, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/workspace/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/workspace/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [20]:
def discriminator(images, reuse=False,alpha=.2,dropout=.8):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    initializer = tf.contrib.layers.xavier_initializer()
    keep_prob = 0.5
       
    with tf.variable_scope('discriminator', reuse=reuse):        
        # Input layer is 14x14x32
                
        
        conv1 = tf.layers.conv2d(images, 32, 4, strides=2, padding='same', kernel_initializer=initializer)
        conv1d = tf.layers.dropout(conv1, rate=(keep_prob), training=True)
        conv1r = tf.maximum(alpha * conv1d, conv1d)
        # 7x7x64
        
        conv2 = tf.layers.conv2d(conv1r, 64, 4, strides=2, padding='same', kernel_initializer=initializer)
        conv2d = tf.layers.dropout(conv2, rate=(keep_prob), training=True)
        conv2n = tf.layers.batch_normalization(conv2d, training=True)
        conv2r = tf.maximum(alpha * conv2n, conv2n)
        # 4x4x128
        
        conv3 = tf.layers.conv2d(conv2r, 128, 4, strides=2, padding='same', kernel_initializer=initializer)
        conv3d = tf.layers.dropout(conv3, rate=(keep_prob), training=True)
        conv3n = tf.layers.batch_normalization(conv3d, training=True)
        conv3r = tf.maximum(alpha * conv3n, conv3n)

        # Flatten it
        flat = tf.reshape(conv3r, (-1, 4*4*128))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [23]:
def generator(z, out_channel_dim, is_train=True,alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    initializer = tf.contrib.layers.xavier_initializer()
    keep_prob = 0.5
          

    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
       
        denseL = tf.layers.dense(z, 4*4*256)
        # Reshape it to start the convolutional stack
        dense_r = tf.reshape(denseL, (-1, 4, 4, 256))
        dense_rn = tf.layers.batch_normalization(dense_r, training=is_train)
        dense_rnr = tf.maximum(alpha * dense_rn, dense_rn)
        
        
        conv_2 = tf.layers.conv2d_transpose(dense_rnr, 64, 4, strides=2, padding='same',
                                            kernel_initializer = initializer)
        conv_2n = tf.layers.batch_normalization(conv_2, training=is_train)
        conv_2nr = tf.maximum(alpha * conv_2n, conv_2n)
        conv_2d = tf.layers.dropout(conv_2nr, rate=(keep_prob), training=is_train)
       
        # 4x4x256 now
        #change from 5 to 4 below
        conv_2 = tf.layers.conv2d_transpose(dense_rnr, 128, 4, strides=1, padding='valid', kernel_initializer=initializer)
        conv_2d = tf.layers.dropout(conv_2, rate=(keep_prob), training=is_train)
        conv_2n = tf.layers.batch_normalization(conv_2d, training=is_train)
        conv_2nr = tf.maximum(alpha * conv_2n,conv_2n)
        # print(x2.shape)
        # 7x7x128 now
        
        conv_3 = tf.layers.conv2d_transpose(conv_2nr, 64, 5, strides=2, padding='same', kernel_initializer=initializer)
        conv_3d = tf.layers.dropout(conv_3, rate=(keep_prob), training=is_train)
        conv_3n = tf.layers.batch_normalization(conv_3d, training=is_train)
        conv_3nr = tf.maximum(alpha * conv_3n, conv_3n)
        # print(x3.shape)
        # 14x14x64 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(conv_3nr, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=initializer)
        # print(logits.shape)
        # 28x28x5 now
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [24]:
def model_loss(input_real, input_z, out_channel_dim,alpha=0.2,dropout=0.8, smooth=0.1):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True,alpha=alpha)
    
    
    d_model_real, d_logits_real = discriminator(input_real, reuse=False, alpha=alpha, dropout=dropout)
       
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True,alpha=alpha, dropout=dropout)
        
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                labels=tf.ones_like(d_model_real) * (1 - smooth)))
      
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.zeros_like(d_model_fake)))
       
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.ones_like(d_model_fake)))
        
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [25]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
   
    t_vars = tf.trainable_variables()
    
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    gen_updates = [op for op in update_ops if op.name.startswith('generator')]
    
    with tf.control_dependencies(gen_updates):
             
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
             
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [26]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [27]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
   
    
    _, img_width, img_height, n_channels = data_shape
    img, z, learn_rate = model_inputs(img_width, img_height, n_channels, z_dim)
    d_loss, g_loss = model_loss(img, z, n_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            
            
            steps = 0
            d_loss_sum = 0
            g_loss_sum = 0
            batch_count = 0
            
            for batch_images in get_batches(batch_size):
                
                steps += 1
                batch_count += 1
                batch_images * 2
                
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                                
                _ = sess.run(d_opt, feed_dict={img: batch_images, z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={z: batch_z, learn_rate: learning_rate})

                d_loss_sum += d_loss.eval({z: batch_z, img: batch_images})
                g_loss_sum += g_loss.eval({z: batch_z,img:batch_images})

                
                if steps% 100 == 0:
                    
                    
                    show_generator_output(sess, 16, z, n_channels, data_image_mode)
                    
                    print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count,steps),
                          "Avg. Discriminator Loss: {:.4f}...".format(d_loss_sum / batch_count),
                          "Avg. Generator Loss: {:.4f}".format(g_loss_sum / batch_count))   
                    
                    
                    d_loss_sum = 0
                    g_loss_sum = 0
                    
                    
                    batch_count = 0

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [28]:
batch_size = 32
z_dim = 120
learning_rate = 0.002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 Step 100... Avg. Discriminator Loss: 1.4854... Avg. Generator Loss: 1.1715
Epoch 1/2 Step 200... Avg. Discriminator Loss: 1.3622... Avg. Generator Loss: 1.0060
Epoch 1/2 Step 300... Avg. Discriminator Loss: 1.2820... Avg. Generator Loss: 1.0259
Epoch 1/2 Step 400... Avg. Discriminator Loss: 1.1598... Avg. Generator Loss: 1.1957
Epoch 1/2 Step 500... Avg. Discriminator Loss: 0.9858... Avg. Generator Loss: 1.5322
Epoch 1/2 Step 600... Avg. Discriminator Loss: 0.8857... Avg. Generator Loss: 1.8740
Epoch 1/2 Step 700... Avg. Discriminator Loss: 0.8318... Avg. Generator Loss: 2.0353
Epoch 1/2 Step 800... Avg. Discriminator Loss: 0.8156... Avg. Generator Loss: 2.0977
Epoch 1/2 Step 900... Avg. Discriminator Loss: 0.7307... Avg. Generator Loss: 2.3122
Epoch 1/2 Step 1000... Avg. Discriminator Loss: 0.6742... Avg. Generator Loss: 2.6184
Epoch 1/2 Step 1100... Avg. Discriminator Loss: 0.6635... Avg. Generator Loss: 2.6696
Epoch 1/2 Step 1200... Avg. Discriminator Loss: 0.5919... Avg. Generator Loss: 2.8370
Epoch 1/2 Step 1300... Avg. Discriminator Loss: 0.5912... Avg. Generator Loss: 3.0805
Epoch 1/2 Step 1400... Avg. Discriminator Loss: 0.5360... Avg. Generator Loss: 3.1665
Epoch 1/2 Step 1500... Avg. Discriminator Loss: 0.6086... Avg. Generator Loss: 3.2481
Epoch 1/2 Step 1600... Avg. Discriminator Loss: 0.5208... Avg. Generator Loss: 3.2785
Epoch 1/2 Step 1700... Avg. Discriminator Loss: 0.5986... Avg. Generator Loss: 3.2007
Epoch 1/2 Step 1800... Avg. Discriminator Loss: 0.6198... Avg. Generator Loss: 2.9616
Epoch 2/2 Step 100... Avg. Discriminator Loss: 0.4537... Avg. Generator Loss: 3.6848
Epoch 2/2 Step 200... Avg. Discriminator Loss: 0.6264... Avg. Generator Loss: 3.4690
Epoch 2/2 Step 300... Avg. Discriminator Loss: 0.4616... Avg. Generator Loss: 3.5029
Epoch 2/2 Step 400... Avg. Discriminator Loss: 0.5552... Avg. Generator Loss: 3.5817
Epoch 2/2 Step 500... Avg. Discriminator Loss: 0.6383... Avg. Generator Loss: 3.4987
Epoch 2/2 Step 600... Avg. Discriminator Loss: 0.4775... Avg. Generator Loss: 3.2715
Epoch 2/2 Step 700... Avg. Discriminator Loss: 0.6028... Avg. Generator Loss: 3.3890
Epoch 2/2 Step 800... Avg. Discriminator Loss: 0.5049... Avg. Generator Loss: 3.4446
Epoch 2/2 Step 900... Avg. Discriminator Loss: 0.4322... Avg. Generator Loss: 4.0131
Epoch 2/2 Step 1000... Avg. Discriminator Loss: 0.5791... Avg. Generator Loss: 3.2930
Epoch 2/2 Step 1100... Avg. Discriminator Loss: 0.4222... Avg. Generator Loss: 3.9562
Epoch 2/2 Step 1200... Avg. Discriminator Loss: 0.4159... Avg. Generator Loss: 4.0019
Epoch 2/2 Step 1300... Avg. Discriminator Loss: 0.4193... Avg. Generator Loss: 4.0628
Epoch 2/2 Step 1400... Avg. Discriminator Loss: 0.5514... Avg. Generator Loss: 3.7742
Epoch 2/2 Step 1500... Avg. Discriminator Loss: 0.4299... Avg. Generator Loss: 3.7960
Epoch 2/2 Step 1600... Avg. Discriminator Loss: 0.6630... Avg. Generator Loss: 3.3599
Epoch 2/2 Step 1700... Avg. Discriminator Loss: 0.4501... Avg. Generator Loss: 3.7606
Epoch 2/2 Step 1800... Avg. Discriminator Loss: 0.4858... Avg. Generator Loss: 3.6878

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [29]:
batch_size = 32
z_dim = 120
learning_rate = 0.002
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1 Step 100... Avg. Discriminator Loss: 1.1673... Avg. Generator Loss: 2.0422
Epoch 1/1 Step 200... Avg. Discriminator Loss: 1.2329... Avg. Generator Loss: 1.2682
Epoch 1/1 Step 300... Avg. Discriminator Loss: 1.3803... Avg. Generator Loss: 0.9433
Epoch 1/1 Step 400... Avg. Discriminator Loss: 1.3543... Avg. Generator Loss: 0.8759
Epoch 1/1 Step 500... Avg. Discriminator Loss: 1.3581... Avg. Generator Loss: 0.8860
Epoch 1/1 Step 600... Avg. Discriminator Loss: 1.3413... Avg. Generator Loss: 0.8840
Epoch 1/1 Step 700... Avg. Discriminator Loss: 1.3447... Avg. Generator Loss: 0.8875
Epoch 1/1 Step 800... Avg. Discriminator Loss: 1.3356... Avg. Generator Loss: 0.8973
Epoch 1/1 Step 900... Avg. Discriminator Loss: 1.3316... Avg. Generator Loss: 0.9047
Epoch 1/1 Step 1000... Avg. Discriminator Loss: 1.3388... Avg. Generator Loss: 0.9045
Epoch 1/1 Step 1100... Avg. Discriminator Loss: 1.3197... Avg. Generator Loss: 0.9166
Epoch 1/1 Step 1200... Avg. Discriminator Loss: 1.2923... Avg. Generator Loss: 0.9392
Epoch 1/1 Step 1300... Avg. Discriminator Loss: 1.3023... Avg. Generator Loss: 0.9519
Epoch 1/1 Step 1400... Avg. Discriminator Loss: 1.2816... Avg. Generator Loss: 0.9456
Epoch 1/1 Step 1500... Avg. Discriminator Loss: 1.2611... Avg. Generator Loss: 1.0255
Epoch 1/1 Step 1600... Avg. Discriminator Loss: 1.2427... Avg. Generator Loss: 0.9910
Epoch 1/1 Step 1700... Avg. Discriminator Loss: 1.2470... Avg. Generator Loss: 0.9913
Epoch 1/1 Step 1800... Avg. Discriminator Loss: 1.2358... Avg. Generator Loss: 1.0128
Epoch 1/1 Step 1900... Avg. Discriminator Loss: 1.2155... Avg. Generator Loss: 1.0554
Epoch 1/1 Step 2000... Avg. Discriminator Loss: 1.2293... Avg. Generator Loss: 1.0485
Epoch 1/1 Step 2100... Avg. Discriminator Loss: 1.2286... Avg. Generator Loss: 1.0274
Epoch 1/1 Step 2200... Avg. Discriminator Loss: 1.2235... Avg. Generator Loss: 1.0575
Epoch 1/1 Step 2300... Avg. Discriminator Loss: 1.2395... Avg. Generator Loss: 1.0673
Epoch 1/1 Step 2400... Avg. Discriminator Loss: 1.2001... Avg. Generator Loss: 1.0888
Epoch 1/1 Step 2500... Avg. Discriminator Loss: 1.2177... Avg. Generator Loss: 1.0780
Epoch 1/1 Step 2600... Avg. Discriminator Loss: 1.1976... Avg. Generator Loss: 1.1019
Epoch 1/1 Step 2700... Avg. Discriminator Loss: 1.1953... Avg. Generator Loss: 1.1177
Epoch 1/1 Step 2800... Avg. Discriminator Loss: 1.1642... Avg. Generator Loss: 1.1312
Epoch 1/1 Step 2900... Avg. Discriminator Loss: 1.1886... Avg. Generator Loss: 1.1559
Epoch 1/1 Step 3000... Avg. Discriminator Loss: 1.1929... Avg. Generator Loss: 1.1414
Epoch 1/1 Step 3100... Avg. Discriminator Loss: 1.1805... Avg. Generator Loss: 1.1420
Epoch 1/1 Step 3200... Avg. Discriminator Loss: 1.1621... Avg. Generator Loss: 1.1600
Epoch 1/1 Step 3300... Avg. Discriminator Loss: 1.1859... Avg. Generator Loss: 1.1294
Epoch 1/1 Step 3400... Avg. Discriminator Loss: 1.1817... Avg. Generator Loss: 1.1747
Epoch 1/1 Step 3500... Avg. Discriminator Loss: 1.1495... Avg. Generator Loss: 1.1643
Epoch 1/1 Step 3600... Avg. Discriminator Loss: 1.1330... Avg. Generator Loss: 1.2002
Epoch 1/1 Step 3700... Avg. Discriminator Loss: 1.1228... Avg. Generator Loss: 1.2231
Epoch 1/1 Step 3800... Avg. Discriminator Loss: 1.1230... Avg. Generator Loss: 1.1997
Epoch 1/1 Step 3900... Avg. Discriminator Loss: 1.1495... Avg. Generator Loss: 1.2251
Epoch 1/1 Step 4000... Avg. Discriminator Loss: 1.1305... Avg. Generator Loss: 1.2128
Epoch 1/1 Step 4100... Avg. Discriminator Loss: 1.1442... Avg. Generator Loss: 1.3043
Epoch 1/1 Step 4200... Avg. Discriminator Loss: 1.1342... Avg. Generator Loss: 1.2375
Epoch 1/1 Step 4300... Avg. Discriminator Loss: 1.0942... Avg. Generator Loss: 1.2894
Epoch 1/1 Step 4400... Avg. Discriminator Loss: 1.1055... Avg. Generator Loss: 1.2725
Epoch 1/1 Step 4500... Avg. Discriminator Loss: 1.0771... Avg. Generator Loss: 1.3324
Epoch 1/1 Step 4600... Avg. Discriminator Loss: 1.1157... Avg. Generator Loss: 1.3035
Epoch 1/1 Step 4700... Avg. Discriminator Loss: 1.1000... Avg. Generator Loss: 1.3595
Epoch 1/1 Step 4800... Avg. Discriminator Loss: 1.0824... Avg. Generator Loss: 1.3415
Epoch 1/1 Step 4900... Avg. Discriminator Loss: 1.0737... Avg. Generator Loss: 1.3422
Epoch 1/1 Step 5000... Avg. Discriminator Loss: 1.0966... Avg. Generator Loss: 1.3791
Epoch 1/1 Step 5100... Avg. Discriminator Loss: 1.0738... Avg. Generator Loss: 1.4224
Epoch 1/1 Step 5200... Avg. Discriminator Loss: 1.0773... Avg. Generator Loss: 1.3857
Epoch 1/1 Step 5300... Avg. Discriminator Loss: 1.0628... Avg. Generator Loss: 1.4105
Epoch 1/1 Step 5400... Avg. Discriminator Loss: 1.0522... Avg. Generator Loss: 1.3946
Epoch 1/1 Step 5500... Avg. Discriminator Loss: 1.0285... Avg. Generator Loss: 1.4316
Epoch 1/1 Step 5600... Avg. Discriminator Loss: 1.0745... Avg. Generator Loss: 1.4130
Epoch 1/1 Step 5700... Avg. Discriminator Loss: 1.0667... Avg. Generator Loss: 1.4065
Epoch 1/1 Step 5800... Avg. Discriminator Loss: 1.0473... Avg. Generator Loss: 1.4797
Epoch 1/1 Step 5900... Avg. Discriminator Loss: 1.0021... Avg. Generator Loss: 1.4559
Epoch 1/1 Step 6000... Avg. Discriminator Loss: 1.0358... Avg. Generator Loss: 1.5235
Epoch 1/1 Step 6100... Avg. Discriminator Loss: 1.0225... Avg. Generator Loss: 1.5037
Epoch 1/1 Step 6200... Avg. Discriminator Loss: 0.9919... Avg. Generator Loss: 1.5336
Epoch 1/1 Step 6300... Avg. Discriminator Loss: 1.0136... Avg. Generator Loss: 1.5160

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

In case of MNIST dataset the discriminator gets strong very soon whereas in the case of celebrity images dataset the discriminator is smae as compaed to generator.

In [ ]: